{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性，初步了解各特征的分布\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W8/QlJ0nSHNXnLqbhdSEeB+4AVoykGknSxOgzBuG6EJI0D0235OhHpjmuquqjI6hHkjQh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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18ef6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标Outcome 分布，看看各类样本分布是否均衡\n",
    "sns.countplot(data.Outcome);\n",
    "plt.xlabel('Outcome');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KQzJSyMtIITs1ibguOiNIeq7Ah/7J5ha+8MRq9h9p5A+fmkn/pMC/JdJLDc5M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\nAPn/5uRYGEIsvbYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18e61eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IjPEyIzvJ7VCMH/7lygv42uWFPLnxIN969h27fTQKDNpCEBEP8CDwUaAa2CQi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\nGOOwhGCMMQawhGCMMcZhCcEYYwwA/x9J7XD7ZGNBwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b57a5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AqRHLtc66Ucuoqh/oAArCynwK2KWqA0752nGOaUxcCwSVw42drF6Q43Yok3JheT5gNyEy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10142ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a103f4518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zgbfOGsfG3YdYv6ueLXuP8MyWvTS3Hnv6YoFgdHkxY4aVMKa8mLdXjWfKmHKm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\ni1hFKpIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1006bdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wu4S5OanUtVtP3zjLQt/EtFdrWgHivqcPUJ6bSkNHH0N+W3HTOMdC38S0HcdbKcjwkp0a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\nCG3QfcPyQqdLMaO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18e43da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DrK8u5pyTSnn1SWWctrBADQ6ZETGFu5m9Afg2EAR+6Jz72ojnM4FbgTOBZuBd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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10335630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R6W2dPVQ3trHv6An2HjnBD17fzSOv7SZgMC+0EriwPIeKsizSEuM8/gtkKL4L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\nxEcU+iIiPqLQFxHxEYW+iIiP/H9gvjIObk/ZOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1035b128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/Nvjz8DsWuVJpqsS6XMakeH3cvQfov1xGIvMOZ53R7iXyKbBfjpnVmNlGM3vX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\n/H8M3BHXtrlGTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0fd27f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 循环遍历特征，得到各个特征的直方图\n",
    "for feature in data.columns:\n",
    "    sns.distplot(data[feature], bins=30, kde=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 9)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ldNoCaj5S3SMm4mgke3fsx15zDqRVFSuVY//+Qxw6eISYmBgm/zyTRo3resQ0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\nFPhOkmc3DTWAJOv6j5uS3Drxa1OSW1vnk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10446278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('diabetes_heatmap.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies and Age = 0.54\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BwSNNJJouWthuJ7m3ZXncPPOc3KO4MR1XAjEtHNduJ5g5bDPAHtv0Lp6Te1Q6XNQuJjEt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\nCRERUTBsQkREFAybEBERBfP/AYoN4HmBqafPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10446630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of only the highly correlated pairs\n",
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(data, size=6, x_vars=cols[i],y_vars=cols[j] )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
